BACKGROUND: Phantom-based quality assurance (QA) of magnetic resonance imaging (MRI) coils is a standard practice for assessing signal-to-noise ratio (SNR), image uniformity, and magnetic field homogeneity. Manual slice selection and region-of-interest (ROI) placements introduce operator-dependent variability and workflow inefficiencies. PURPOSE: To develop a unified, fully automated desktop-based workflow for MRI coil QA that eliminates manual slice selection and ROI placement. MATERIALS AND METHODS: A Python desktop app was built to automate MRI coil QA workflows for body, torso, and head-and-neck coils. It takes DICOM image sets and raw free-induction decay data and performs slice selection, phantom detection, and fixed-geometry ROI placement automatically to calculate standard QA metrices: SNR, percent image uniformity, and frequency-domain magnetic field homogeneity. Thus, obtained results are directly saved in structured reports and plots. RESULTS: The automated workflow carried out all implemented QA tasks, without manual ROI placement, and consistently produced QA metrices for body, torso, head-and-neck, weekly QA, and magnetic field homogeneity analyses. The standardization of the outputs facilitated an objective evaluation of the coils and channel performance by eliminating the subjective bias. CONCLUSIONS: This study presents an automated MRI quality assurance workflow that operates through standardized testing methods and delivers consistent results while decreasing the impact of human error in standard clinical quality control procedures.
Paliwal et al. (Fri,) studied this question.